hello.py

@@ -0,0 +1,6 @@
+def main():
+    print("Hello from experiment!")
+
+
+if __name__ == "__main__":
+    main()

main.py

@@ -1,79 +0,0 @@
-"""Write a short description of your experiment here."""
-
-import logging
-import lightning as L
-import torch
-from torch.utils.data import DataLoader, random_split
-
-from protothor.callbacks.normalization import OmegaTraceNormalization
-from protothor.data.preprocessing import Standardizer
-from lightning.pytorch.callbacks import EarlyStopping
-from protothor.data.toy import Iris
-from protothor.functional.initialization import zero_initialization
-from protothor.lightning.metric_module import MetricModule
-from protothor.models.gmlvq import generate_model as generate_gmlvq
-from protothor.nn.collectable_loss import EvaluateExposedLosses
-from protothor.nn.container import find_instances
-
-logging.basicConfig(level=logging.INFO)
-
-
-def main():
-    # 1 - Get Dataset
-    data = Iris([0, 2])
-    train_ds, val_ds = random_split(data, [0.7, 0.3])
-
-    standardize = Standardizer(train_ds)
-
-    # 2 - Create Dataloaders
-    train_loader = DataLoader(
-        standardize(train_ds),
-        shuffle=True,
-        batch_size=len(train_ds),
-    )
-    val_loader = DataLoader(
-        standardize(val_ds),
-        shuffle=False,
-        batch_size=len(val_ds),
-    )
-
-    # 3 - Initialize Prototypes
-    labels, positions = zero_initialization(data)
-
-    # 4 - Generate Torch Model
-    model = generate_gmlvq(positions, labels)
-    omega_layer = find_instances(model, torch.nn.Linear)[0]
-
-    # 5 - Initialize Lightning Module
-    module = MetricModule(model, EvaluateExposedLosses(model))
-
-    # 6 - Define Callbacks
-
-    callbacks = [
-        OmegaTraceNormalization(omega_layer),
-    ]
-    callbacks.append(
-        EarlyStopping(
-            monitor="accuracy/validation",
-            min_delta=0.0002,
-            patience=50,
-            mode="max",
-            verbose=False,
-            check_on_train_epoch_end=True,
-        )
-    )
-
-    # 7 - Define Trainer
-    trainer = L.Trainer(detect_anomaly=True, max_epochs=1000, callbacks=callbacks)
-
-    # 8 - Train Model
-    trainer.fit(module, train_loader, val_loader)
-
-    # 9 - Analyse results
-    omega = omega_layer.weight.detach()
-    classification_correlation_matrix = omega.T @ omega
-    logging.info(classification_correlation_matrix)
-
-
-if __name__ == "__main__":
-    main()

pyproject.toml

@@ -4,26 +4,4 @@ version = "0.1.0"
 description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.12"
-dependencies = [
+dependencies = []
-    "protothor>=0.1.1",
-    "protothor-datarecords>=0.8.1",
-]
-
-[tool.uv.sources]
-protothor = [{index = "protothor-gitlab"}]
-protothor-datarecords = [{index = "protothor-datarecords-gitlab"}]
-
-[[tool.uv.index]]
-name = "protothor-datarecords-gitlab"
-url = "https://git.hs-mittweida.de/api/v4/projects/2428/packages/pypi/simple"
-default = false
-
-[[tool.uv.index]]
-name = "protothor-gitlab"
-url = "https://git.hs-mittweida.de/api/v4/projects/2346/packages/pypi/simple"
-default = false
-
-[[tool.uv.index]]
-name = "torch-cpu"
-url = "https://download.pytorch.org/whl/cpu"
-default = false